Since deformation estimation may lead to errors occurring when the camera vibrates, it is necessary to remove the image global motion before computing real bridge deformation. In this study, a combination of image motion correction algorithm and 2D image-based deformation measurement technique was utilized to address the issue of camera motion during the image data acquisition for bridge deformation measurement. Based on the proposed methodology, the image motion parameters were estimated by defining an effective sub-image in the image and using Iterative Affine Motion Estimator. Then the estimated parameters were applied to all pixels of each captured image to remove the motion effect. Finally, the corrected images were used to analyze by a 2D image-based deformation measurement technique in order to extract and measure real bridge deformation by tracking artificial or natural targets. The proposed methodology was validated by two experiments in the lab and field environments. Achieved results show the accuracy and reliability of the proposed methodology.

In this paper, we present an algorithm to simultaneously estimate multiple-frame motions and filter image sequences. The relative motion dk(x) between the reference frame s(x) and the kth frame s(x-dk(x)) in the presence of additive white Gaussian noise (AWGN) is estimated using the maximum-likelihood (ML) principle. The reference frame is also filtered in the linear minimum mean square error (LMMSE) sense during the process of motion estimation. Simulation experiments are performed using the affine motion model to illustrate the performance of this method.

Endoscopes are crucial for assisting in surgery and disease diagnosis, including the early detection of cancer. The effective use of endoscopes relies on their optical performance, which can be characterized with a series of metrics such as resolution, vital for revealing anatomical details. The modulation transfer function (MTF) is a key metric for evaluating endoscope resolution. However, the 2020 version of the ISO 8600-5 standard, while introducing an endoscope MTF measurement method, lacks empirical validation and excludes opto-electronic video endoscopes, the largest family of endoscopes. Measuring the MTF of video endoscopes requires tailored standards that address their unique characteristics. This paper aims to expand the scope of ISO 8600-5:2020 to include video endoscopes, by optimizing the MTF test method and addressing parameters affecting measurement accuracy. We studied the effects of intensity and uniformity of image luminance, chart modulation compensation, linearity of image digital values, auto gain control, image enhancement, image compression and the region of interest dimensions on images of slanted-edge test charts, and thus the MTF based on these images. By analyzing these effects, we provided recommendations for setting and controlling these factors to obtain accurate MTF curves. Our goal is to enhance the standard’s relevance and effectiveness for measuring the MTF of a broader range of endoscopic devices, with potential applications in the MTF measurement of other digital imaging devices.

A non-contact optical measurement system using a sub-pixel motion correction and target tracking algorithm is proposed in order to remove the probable camera vibration during the measurement test and calculate the real bridge deflection. To achieve this goal, an image correction algorithm is utilized to estimate and correct the global motion of the camera. Subsequently, computer vision and image processing algorithms are used to measure the deformation of the bridge structure. Two experimental studies are conducted and presented to validate the applicability and accuracy of the proposed methodology in the laboratory and in the field. The results showed that this approach can be utilized accurately as a step toward solving the problem of structural deflection measurement with considering camera motion.

This paper develops, presents, and evaluates a new iterative algorithm to estimate image motion from noisy-blurred image sequences. This algorithm seeks the maximum-likelihood estimate of image motion coefficients in an arbitrary transformed domain directly from noisy-blurred data. The range of the convergence parameters that control the speed of convergence is also presented. Simulation experiments demonstrate the validity of the motion estimator. Additional experiments investigate the algorithm's sensitivity to the extent of blur and noise. We also show the potential application of the estimated motion to image sequence restoration.